Conventional body-monitoring systems employ sensors to measure one or more variables relating to physiological signals produced by an individual's body. These systems typically output information relating to measured variables such that the individual or a healthcare provider responsible for the individual may monitor the information and take any necessary actions to achieve an objective.
Body-monitoring systems often monitor the same or similar variables for a plurality of objectives. As an example, blood pressure may be monitored and displayed (e.g., in time series) in a critical care medical environment such that a physician may intervene when the variable indicates a health risk. As another example, an individual may monitor their own blood pressure in a home environment in order to take a precautionary action, such as ingesting a medicine, resting, changing their diet and/or exercising. And, as yet another example, an individual may monitor their heart rate during physical activity to adjust pace or resistance.
In each of the above examples, an individual (i.e., the user or a surrogate) must take an action in response to the monitored variable. From a systems perspective, these examples represent open-loop systems because they require an individual to be an active part of the feedback loop.
With the development of new processing and sensor technologies, biotechnology companies are developing closed-loop systems and devices to monitor and automatically regulate physiological processes. Unfortunately, the sensors employed by such systems cannot monitor component neural influences embedded in monitored bio-signals. As a result, these systems often provide feedback conveying a poor approximation of the signal that the nervous system is anticipating.
Bio-signals produced by visceral organs and other body or brain structures are complex and usually represent a composite of several underlying mechanisms, including endocrine and neural influences. Although the sensors employed in currently-available, closed-loop systems may accurately detect level, the surveillance pathways embedded in the nervous system (i.e., afferent pathways that convey information about target organs to brainstem regulatory mechanisms) may be sensitive to the characteristics of the temporal pattern of change (i.e., the temporal window during which changes—including levels and complex periodic and aperiodic patterns—are induced). Accordingly, feedback that changes level in the process, signal, or variable being monitored (e.g., delivery of a pharmaceutical or neurostimulation) may inadvertently distort, rather than optimize, the desired trajectory. Without being knowledgeable that the nervous system incorporates circuits that may functionally anticipate a physiological signature (i.e., a neurophysiologically informed pattern), well-intentioned, closed-loop designs may inappropriately shift levels of a target system causing disruption to the nervous system.
For example, manipulations that change slope and level of a relatively stable signal will introduce quasi-periodic components into the time series. The resulting quasi-periodic time series will have statistical characteristics that distinguish it from the more sinusoidal, rhythmic patterns observed in well-regulated, physiological systems that characterize many homeostatic processes. Moreover, although an induced quasi-periodic time series may have a physiologically relevant, fundamental oscillation, it may also include higher frequency harmonics that convey non-relevant physiological information.
As patterns deviate from the more rhythmic neurophysiological expectations, the neurophysiological sensors embedded in the mammalian nervous system are less likely to be able to decode the meaning of these sources of variance. Thus, the deviations from expectations may confuse the endogenous sensors embedded in the nervous system that evolved to regulate these systems and to maintain homeostatic function to optimize health, growth, and restoration. In contrast to a surveillance of shifts in levels, the nervous system may expect a more complex signal including a combination of periodic oscillations of varying amplitudes, periodicities, and slopes.
Accordingly, there is a need for closed-loop systems that are adapted to monitor and automatically regulate physiological structures and that employ efficient and accurate feedback indexing the dynamically changing neural influences on the monitored physiological structures. It would be beneficial if such systems were adapted to shift neural regulation of a specific physiological structure and/or a more general physiological state of a subject to promote and influence emergent properties that support various outcomes relating to, for example, health, positive affect, alertness, spontaneous interactions with others, attentiveness, mental effort and/or physical exertion.